Reducing the Number of Training Cases in Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Zoppi, Giacomo
Data de Publicação: 2022
Outros Autores: Vanneschi, Leonardo, Giacobini, Mario
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/146561
Resumo: Zoppi, G., Vanneschi, L., & Giacobini, M. (2022). Reducing the Number of Training Cases in Genetic Programming. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. https://doi.org/10.1109/CEC55065.2022.9870327
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spelling Reducing the Number of Training Cases in Genetic ProgrammingTrainingBoolean functionsGenetic programmingMachine learningEvolutionary computationData modelsBenchmark testingArtificial IntelligenceComputer Science ApplicationsComputational MathematicsControl and OptimizationZoppi, G., Vanneschi, L., & Giacobini, M. (2022). Reducing the Number of Training Cases in Genetic Programming. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. https://doi.org/10.1109/CEC55065.2022.9870327In the field of Machine Learning, one of the most common and discussed questions is how to choose an adequate number of data observations, in order to train our models satisfactorily. In other words, find what is the right amount of data needed to create a model, that is neither underfitted nor overfitted, but instead is able to achieve a reasonable generalization ability. The problem grows in importance when we consider Genetic Programming, where fitness evaluation is often rather slow. Therefore, finding the minimum amount of data that enables us to discover the solution to a given problem could bring significant benefits. Using the notion of entropy in a dataset, we seek to understand the information gain obtainable from each additional data point. We then look for the smallest percentage of data that corresponds to enough information to yield satisfactory results. We present, as a first step, an example derived from the state of art. Then, we question a relevant part of our procedure and introduce two case studies to experimentally validate our theoretical hypothesis.Institute of Electrical and Electronics Engineers (IEEE)Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNZoppi, GiacomoVanneschi, LeonardoGiacobini, Mario2022-12-22T22:23:39Z2022-07-182022-07-18T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion8application/pdfhttp://hdl.handle.net/10362/146561eng978-1-6654-6708-7PURE: 46476290https://doi.org/10.1109/CEC55065.2022.9870327info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:07:36Zoai:run.unl.pt:10362/146561Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:38:14.030153Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Reducing the Number of Training Cases in Genetic Programming
title Reducing the Number of Training Cases in Genetic Programming
spellingShingle Reducing the Number of Training Cases in Genetic Programming
Zoppi, Giacomo
Training
Boolean functions
Genetic programming
Machine learning
Evolutionary computation
Data models
Benchmark testing
Artificial Intelligence
Computer Science Applications
Computational Mathematics
Control and Optimization
title_short Reducing the Number of Training Cases in Genetic Programming
title_full Reducing the Number of Training Cases in Genetic Programming
title_fullStr Reducing the Number of Training Cases in Genetic Programming
title_full_unstemmed Reducing the Number of Training Cases in Genetic Programming
title_sort Reducing the Number of Training Cases in Genetic Programming
author Zoppi, Giacomo
author_facet Zoppi, Giacomo
Vanneschi, Leonardo
Giacobini, Mario
author_role author
author2 Vanneschi, Leonardo
Giacobini, Mario
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Zoppi, Giacomo
Vanneschi, Leonardo
Giacobini, Mario
dc.subject.por.fl_str_mv Training
Boolean functions
Genetic programming
Machine learning
Evolutionary computation
Data models
Benchmark testing
Artificial Intelligence
Computer Science Applications
Computational Mathematics
Control and Optimization
topic Training
Boolean functions
Genetic programming
Machine learning
Evolutionary computation
Data models
Benchmark testing
Artificial Intelligence
Computer Science Applications
Computational Mathematics
Control and Optimization
description Zoppi, G., Vanneschi, L., & Giacobini, M. (2022). Reducing the Number of Training Cases in Genetic Programming. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. https://doi.org/10.1109/CEC55065.2022.9870327
publishDate 2022
dc.date.none.fl_str_mv 2022-12-22T22:23:39Z
2022-07-18
2022-07-18T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/146561
url http://hdl.handle.net/10362/146561
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-6654-6708-7
PURE: 46476290
https://doi.org/10.1109/CEC55065.2022.9870327
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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